Ionospheric total electron content response to annular solar eclipse on June 21, 2020

The effects of physical events on the ionosphere structure is an important field of study, especially for navigation and radio communication. The paper presents the spatio-temporal ionospheric TEC response to the recent annular solar eclipse on June 21, 2020, which spans across two continents, Africa and Asia, and 14 countries. This eclipse took place on the same day as the June Solstice. The Global Navigation Satellite System (GNSS) based TEC data of the Global Ionosphere Maps (GIMs), 9 International GNSS Service (IGS) stations and FORMOSAT-7/COSMIC-2 (F7/C2) were utilized to analyze TEC response during the eclipse. The phases of the TEC time series were determined by taking the difference of the observed TEC values on eclipse day from the previous 5-day median TEC values. The results showed clear depletions in the TEC time series on June 21. These decreases were between 1 and 9 TECU (15–60%) depending on the location of IGS stations. The depletions are relatively higher at the stations close to the path of annular eclipse than those farther away. Furthermore, a reduction of about ?10 TECU in the form of an equatorial plasma bubble (EPB) was observed in GIMs at ~20° away from the equator towards northpole, between 08:00–11:00 UT where its maximum phase is located in southeast Japan. Additionally, an overall depletion of ~10% was observed in F7/C2 derived TEC at an altitude of 240 km (hmF2) in all regions affected by the solar eclipse, whereas, significant TEC fluctuations between the altitudes of 100 km ? 140 km were analyzed using the Savitzky-Golay smoothing filter. To prove TEC depletions are not caused by space weather, the variation of the sunspot number (SSN), solar wind (V_SW), disturbance storm-time (Dst), and Kp indices were investigated from 16th to 22nd June. The quiet space weather before and during the solar eclipse proved that the observed depletions in the TEC time series and profiles were caused by the annular solar eclipse.     

Determination of land surface temperature using precipitable water based Split-Window and Artificial Neural Network in Turkey

Land surface temperature (LST) calculation utilizing satellite thermal images is very difficult due to the great temporal variance of atmospheric water vapor in the atmosphere which strongly affects the thermal radiance incoming to satellite sensors. In this study, Split-Window (SW) and Radial Basis Function (RBF) methods were utilized for prediction of LST using precipitable water for Turkey. Coll 94 Split-Window algorithm was modified using regional precipitable water values estimated from upper-air Radiosond observations for the years 1990–2007 and Local Split-Window (LSW) algorithms were generated for the study area. Using local algorithms and Advanced Very High Resolution Radiometer (AVHRR) data, monthly mean daily sum LST values were calculated. In RBF method latitude, longitude, altitude, surface emissivity, sun shine duration and precipitable water values were used as input variables of the structure. Correlation coefficients between estimated and measured LST values were obtained as 99.23% (for RBF) and 94.48% (for LSW) at 00:00 UTC and 92.77% (for RBF) and 89.98% (for LSW) at 12:00 UTC. These meaningful statistical results suggest that RBF and LSW methods could be used for LST calculation.     

Multipath Separation-Direction of Arrival (MS-DOA) with Genetic Search Algorithm for HF channels

Direction-of-Arrival (DOA) defines the estimation of arrival angles of an electromagnetic wave impinging on a set of sensors. For dispersive and time-varying HF channels, where the propagating wave also suffers from the multipath phenomena, estimation of DOA is a very challenging problem. Multipath Separation-Direction of Arrival (MS-DOA), that is developed to estimate both the arrival angles in elevation and azimuth and the incoming signals at the output of the reference antenna with very high accuracy, proves itself as a strong alternative in DOA estimation for HF channels. In MS-DOA, a linear system of equations is formed using the coefficients of the basis vector for the array output vector, the incoming signal vector and the array manifold. The angles of arrival in elevation and azimuth are obtained as the maximizers of the sum of the magnitude squares of the projection of the signal coefficients on the column space of the array manifold. In this study, alternative Genetic Search Algorithms (GA) for the maximizers of the projection sum are investigated using simulated and experimental ionospheric channel data. It is observed that GA combined with MS-DOA is a powerful alternative in online DOA estimation and can be further developed according to the channel characteristics of a specific HF link.     

Long term and safe relative orbit design for heterogeneous spacecraft clusters

The problem of obtaining long term relative orbit configurations for spacecraft clusters with realistic operational considerations such as safety, station keeping and inter-spacecraft distances is addressed. Two different approaches are developed for station keeping and safety objectives. In the first approach, relative orbit configurations, or relative TLEs, are found minimizing deviations from reference mean orbit which would maximize the station-keeping objective. In second one, relative configurations are found from a reference initial condition by minimizing probability of collision, hence maximizing the safety objective, between the spacecraft in the cluster which are propagated numerically through a high precision orbit propagator. For the design optimization, a derivative free algorithm is proposed. Effectiveness of the approaches is demonstrated through simulations. Using this design framework, several configurations can be found by exploring the limits of the clusters in terms of spacecraft number, distance bounds and probabilities of collision for long time intervals.     

Modelling of air temperature using remote sensing and artificial neural network in Turkey

The aim of this research was to forecast monthly mean air temperature based on remote sensing and artificial neural network (ANN) data by using twenty cities over Turkey. ANN contained an input layer, hidden layer and an output layer. While city, month, altitude, latitude, longitude, monthly mean land surface temperatures were chosen as inputs, and monthly mean air temperature was chosen as output for network. Levenberg–Marquardt (LM) learning algorithms and tansig, logsig and linear transfer functions were used in the network. The data of Turkish State Meteorological Service (TSMS) and Technological Research Council of Turkey–Bilten for the period from 1995 to 2004 were chosen as training when the data of 2005 year were being used as test. Result of research was evaluated according to statistical rules. The best linear correlation coefficient (R), and root mean squared error (RMSE) between the estimated and measured values for monthly mean air temperature with ANN and remote sensing method were found to be 0.991–1.254 K, respectively.     

Integrating local multi-criteria evaluation with spatially explicit uncertainty-sensitivity analysis

The conventional methods of Multi-Criteria Evaluation (MCE) fail to account for local variability in criteria values and criteria preferences, disregarding local influence in spatial decision making. Moreover, understanding the interaction between MCE model input and output has paramount importance in terms of the level of confidence in the model results and their subsequent use in decision making. This research contributes to spatial MCE methodology by articulating the presence of uncertainty included in criteria weights and extending a local MCE technique with a spatially explicit uncertainty and sensitivity analysis. The article presents a methodology for multi-criteria evaluation extended by an integrated uncertainty-sensitivity analysis and illustrates it on the example of a land prioritization model for conservation practices.     

Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data

In this paper, the estimation capacities of MLR and ANN are investigated to estimate monthly-average daily SR over Turkey. The satellite data are used for 73 different locations over Turkey. Land surface temperature, altitude, latitude, longitude and month are offered as the input variables for modeling ANN and MLR to get SR. Estimations of SR are evaluated with the meteorological values by using the statistical bases. The obtained results indicated that the ANN model could achieve a satisfactory performance when compared to the MLR model. Moreover, it is understood that more accurate results in estimation of SR are obtained in the use of satellite data, rather than the use of meteorological station data. Finally, the built ANN model is used to estimate the yearly average of daily SR over Turkey. As a result, satellite-based SR map for Turkey is generated.